IEEE Trans Image Process. 2016 Nov;25(11):5035-5049. doi: 10.1109/TIP.2016.2598680.
Foreground detection is fundamental in surveillance video analysis and meaningful toward object tracking and higher level tasks, such as anomaly detection and activity analysis. Nevertheless, existing methods are still limited in accurately detecting the foreground due to the complex scene settings. To robustly handle the diverse background variations and foreground challenges, this paper proposes a Background REpresentation approach With Dictionary Learning and Historical Pixel Maintenance (BREW-DLHPM). Specifically, a dictionary learning problem is formulated at the frame level to adaptively represent the background signals with the varied structure information captured, while a pixel-level maintenance is exploited to grasp the dynamic nature of historical information under the help of the learned background. The simultaneous utilization of dictionary learning and historical pixel maintenance facilitates the accurate description of the background and thus guides a wise foreground detection decision. The proposed BREW-DLHPM has been evaluated on the prestigious change detection challenge data set against 11 state-of-the-art foreground detection approaches and encouraging performances have been achieved by our method.
前景检测是监控视频分析的基础，对于目标跟踪和更高层次的任务（如异常检测和活动分析）具有重要意义。然而，由于复杂的场景设置，现有的方法在准确检测前景方面仍然存在局限性。为了稳健地处理不同的背景变化和前景挑战，本文提出了一种基于字典学习和历史像素维护的背景表示方法（BREW-DLHPM）。具体来说，在帧级制定了一个字典学习问题，以自适应地表示捕获的具有变化结构信息的背景信号，同时利用历史信息的像素级维护，在学习的背景的帮助下掌握历史信息的动态特性。字典学习和历史像素维护的同时利用有助于准确描述背景，从而指导明智的前景检测决策。我们的方法在著名的变化检测挑战数据集上与 11 种最先进的前景检测方法进行了评估，取得了令人鼓舞的性能。
